Overview

Dataset statistics

Number of variables14
Number of observations61
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory116.0 B

Variable types

Numeric13
Categorical1

Alerts

kmeans_labels has constant value ""Constant
Flavanoids is highly overall correlated with OD280 and 2 other fieldsHigh correlation
OD280 is highly overall correlated with Flavanoids and 1 other fieldsHigh correlation
Proanthocyanins is highly overall correlated with FlavanoidsHigh correlation
Total_Phenols is highly overall correlated with Flavanoids and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-11-28 13:23:09.452521
Analysis finished2023-11-28 13:23:39.266209
Duration29.81 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Alcohol
Real number (ℝ)

Distinct42
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.190656
Minimum11.03
Maximum12.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:39.384711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.56
Q111.84
median12.29
Q312.47
95-th percentile12.77
Maximum12.86
Range1.83
Interquartile range (IQR)0.63

Descriptive statistics

Standard deviation0.40474218
Coefficient of variation (CV)0.033201018
Kurtosis-0.1538192
Mean12.190656
Median Absolute Deviation (MAD)0.29
Skewness-0.50179584
Sum743.63
Variance0.16381623
MonotonicityNot monotonic
2023-11-28T13:23:39.589149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
12.37 6
 
9.8%
12.08 5
 
8.2%
12.29 3
 
4.9%
12.6 2
 
3.3%
11.84 2
 
3.3%
12.51 2
 
3.3%
11.82 2
 
3.3%
12.72 2
 
3.3%
12 2
 
3.3%
12.42 2
 
3.3%
Other values (32) 33
54.1%
ValueCountFrequency (%)
11.03 1
1.6%
11.41 1
1.6%
11.45 1
1.6%
11.56 1
1.6%
11.61 1
1.6%
11.62 1
1.6%
11.64 1
1.6%
11.65 1
1.6%
11.66 1
1.6%
11.76 1
1.6%
ValueCountFrequency (%)
12.86 1
1.6%
12.85 1
1.6%
12.81 1
1.6%
12.77 1
1.6%
12.72 2
3.3%
12.69 1
1.6%
12.67 1
1.6%
12.64 1
1.6%
12.6 2
3.3%
12.58 1
1.6%

Malic_Acid
Real number (ℝ)

Distinct53
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6488525
Minimum0.74
Maximum2.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:39.792425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile0.94
Q11.29
median1.53
Q32.05
95-th percentile2.55
Maximum2.89
Range2.15
Interquartile range (IQR)0.76

Descriptive statistics

Standard deviation0.52227419
Coefficient of variation (CV)0.3167501
Kurtosis-0.42089753
Mean1.6488525
Median Absolute Deviation (MAD)0.34
Skewness0.56662748
Sum100.58
Variance0.27277033
MonotonicityNot monotonic
2023-11-28T13:23:39.979960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.61 3
 
4.9%
1.13 2
 
3.3%
1.53 2
 
3.3%
1.51 2
 
3.3%
1.73 2
 
3.3%
1.29 2
 
3.3%
1.35 2
 
3.3%
1.6 1
 
1.6%
2.68 1
 
1.6%
1.34 1
 
1.6%
Other values (43) 43
70.5%
ValueCountFrequency (%)
0.74 1
1.6%
0.89 1
1.6%
0.92 1
1.6%
0.94 1
1.6%
0.98 1
1.6%
0.99 1
1.6%
1.07 1
1.6%
1.09 1
1.6%
1.1 1
1.6%
1.13 2
3.3%
ValueCountFrequency (%)
2.89 1
1.6%
2.83 1
1.6%
2.68 1
1.6%
2.55 1
1.6%
2.46 1
1.6%
2.45 1
1.6%
2.43 1
1.6%
2.4 1
1.6%
2.39 1
1.6%
2.31 1
1.6%

Ash
Real number (ℝ)

Distinct45
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2604918
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:40.185920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.88
Q12.1
median2.26
Q32.42
95-th percentile2.74
Maximum3.23
Range1.87
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.30119443
Coefficient of variation (CV)0.13324288
Kurtosis1.8406872
Mean2.2604918
Median Absolute Deviation (MAD)0.16
Skewness0.22290075
Sum137.89
Variance0.090718087
MonotonicityNot monotonic
2023-11-28T13:23:40.382386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
2.28 4
 
6.6%
2.2 4
 
6.6%
2.3 3
 
4.9%
2.42 2
 
3.3%
2.32 2
 
3.3%
2.46 2
 
3.3%
2.17 2
 
3.3%
1.98 2
 
3.3%
2.5 2
 
3.3%
2.1 2
 
3.3%
Other values (35) 36
59.0%
ValueCountFrequency (%)
1.36 1
1.6%
1.7 1
1.6%
1.75 1
1.6%
1.88 1
1.6%
1.9 1
1.6%
1.92 2
3.3%
1.94 1
1.6%
1.95 1
1.6%
1.98 2
3.3%
1.99 1
1.6%
ValueCountFrequency (%)
3.23 1
1.6%
2.92 1
1.6%
2.78 1
1.6%
2.74 1
1.6%
2.7 1
1.6%
2.62 1
1.6%
2.58 1
1.6%
2.56 1
1.6%
2.53 1
1.6%
2.52 1
1.6%

Ash_Alcanity
Real number (ℝ)

Distinct29
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.970492
Minimum10.6
Maximum28.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:40.589283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile16
Q118
median19.6
Q321.5
95-th percentile24.5
Maximum28.5
Range17.9
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.005625
Coefficient of variation (CV)0.1505033
Kurtosis2.0076999
Mean19.970492
Median Absolute Deviation (MAD)1.6
Skewness0.32752124
Sum1218.2
Variance9.0337814
MonotonicityNot monotonic
2023-11-28T13:23:40.775519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
18 6
 
9.8%
19 6
 
9.8%
21 5
 
8.2%
20 4
 
6.6%
18.5 4
 
6.6%
16 3
 
4.9%
22.5 3
 
4.9%
21.5 3
 
4.9%
28.5 2
 
3.3%
16.8 2
 
3.3%
Other values (19) 23
37.7%
ValueCountFrequency (%)
10.6 1
 
1.6%
14.8 1
 
1.6%
16 3
4.9%
16.8 2
 
3.3%
17.5 2
 
3.3%
17.8 1
 
1.6%
18 6
9.8%
18.1 1
 
1.6%
18.5 4
6.6%
18.8 1
 
1.6%
ValueCountFrequency (%)
28.5 2
3.3%
26 1
 
1.6%
24.5 1
 
1.6%
24 2
3.3%
23.6 1
 
1.6%
22.8 1
 
1.6%
22.5 3
4.9%
22 2
3.3%
21.6 1
 
1.6%
21.5 3
4.9%

Magnesium
Real number (ℝ)

Distinct29
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.377049
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:40.966923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80
Q186
median90
Q399
95-th percentile134
Maximum162
Range92
Interquartile range (IQR)13

Descriptive statistics

Standard deviation16.742326
Coefficient of variation (CV)0.1755383
Kurtosis5.3073103
Mean95.377049
Median Absolute Deviation (MAD)6
Skewness2.1172186
Sum5818
Variance280.30546
MonotonicityNot monotonic
2023-11-28T13:23:41.172484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
88 8
 
13.1%
86 7
 
11.5%
85 5
 
8.2%
98 4
 
6.6%
94 3
 
4.9%
84 3
 
4.9%
103 3
 
4.9%
78 2
 
3.3%
92 2
 
3.3%
97 2
 
3.3%
Other values (19) 22
36.1%
ValueCountFrequency (%)
70 1
 
1.6%
78 2
 
3.3%
80 2
 
3.3%
81 1
 
1.6%
84 3
 
4.9%
85 5
8.2%
86 7
11.5%
87 1
 
1.6%
88 8
13.1%
90 2
 
3.3%
ValueCountFrequency (%)
162 1
 
1.6%
151 1
 
1.6%
136 1
 
1.6%
134 1
 
1.6%
122 1
 
1.6%
119 1
 
1.6%
112 1
 
1.6%
108 1
 
1.6%
104 1
 
1.6%
103 3
4.9%

Total_Phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1377049
Minimum1.1
Maximum3.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:41.361520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.38
Q11.72
median2.11
Q32.48
95-th percentile3.18
Maximum3.52
Range2.42
Interquartile range (IQR)0.76

Descriptive statistics

Standard deviation0.55230847
Coefficient of variation (CV)0.25836516
Kurtosis0.11169243
Mean2.1377049
Median Absolute Deviation (MAD)0.39
Skewness0.4959373
Sum130.4
Variance0.30504464
MonotonicityNot monotonic
2023-11-28T13:23:41.575058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
2.2 5
 
8.2%
2 3
 
4.9%
2.48 2
 
3.3%
2.55 2
 
3.3%
2.5 2
 
3.3%
1.98 2
 
3.3%
1.38 2
 
3.3%
1.45 2
 
3.3%
1.6 2
 
3.3%
2.74 2
 
3.3%
Other values (35) 37
60.7%
ValueCountFrequency (%)
1.1 1
1.6%
1.15 1
1.6%
1.38 2
3.3%
1.39 1
1.6%
1.45 2
3.3%
1.48 1
1.6%
1.51 1
1.6%
1.6 2
3.3%
1.61 1
1.6%
1.62 1
1.6%
ValueCountFrequency (%)
3.52 1
1.6%
3.5 1
1.6%
3.38 1
1.6%
3.18 1
1.6%
3.02 1
1.6%
2.9 1
1.6%
2.74 2
3.3%
2.6 1
1.6%
2.56 2
3.3%
2.55 2
3.3%

Flavanoids
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9136066
Minimum0.51
Maximum5.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:41.777734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.51
5-th percentile0.58
Q11.46
median1.94
Q32.26
95-th percentile3.1
Maximum5.08
Range4.57
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.7844213
Coefficient of variation (CV)0.40991775
Kurtosis3.56387
Mean1.9136066
Median Absolute Deviation (MAD)0.36
Skewness1.0286042
Sum116.73
Variance0.61531678
MonotonicityNot monotonic
2023-11-28T13:23:41.971303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.27 2
 
3.3%
2.65 2
 
3.3%
1.25 2
 
3.3%
2.03 2
 
3.3%
1.69 2
 
3.3%
2.26 2
 
3.3%
2.17 2
 
3.3%
1.09 2
 
3.3%
0.58 2
 
3.3%
2.24 1
 
1.6%
Other values (42) 42
68.9%
ValueCountFrequency (%)
0.51 1
1.6%
0.57 1
1.6%
0.58 2
3.3%
0.66 1
1.6%
0.99 1
1.6%
1.02 1
1.6%
1.09 2
3.3%
1.25 2
3.3%
1.28 1
1.6%
1.32 1
1.6%
ValueCountFrequency (%)
5.08 1
1.6%
3.75 1
1.6%
3.15 1
1.6%
3.1 1
1.6%
2.92 1
1.6%
2.79 1
1.6%
2.65 2
3.3%
2.53 1
1.6%
2.5 1
1.6%
2.45 1
1.6%

Nonflavanoid_Phenols
Real number (ℝ)

Distinct31
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37459016
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:42.168771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.17
Q10.28
median0.37
Q30.47
95-th percentile0.6
Maximum0.66
Range0.53
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.13123228
Coefficient of variation (CV)0.35033564
Kurtosis-0.52203195
Mean0.37459016
Median Absolute Deviation (MAD)0.1
Skewness0.2846398
Sum22.85
Variance0.017221913
MonotonicityNot monotonic
2023-11-28T13:23:42.348250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.37 5
 
8.2%
0.26 4
 
6.6%
0.34 3
 
4.9%
0.29 3
 
4.9%
0.48 3
 
4.9%
0.4 3
 
4.9%
0.3 3
 
4.9%
0.43 3
 
4.9%
0.32 3
 
4.9%
0.14 2
 
3.3%
Other values (21) 29
47.5%
ValueCountFrequency (%)
0.13 1
 
1.6%
0.14 2
3.3%
0.17 1
 
1.6%
0.19 1
 
1.6%
0.21 1
 
1.6%
0.22 1
 
1.6%
0.24 1
 
1.6%
0.25 1
 
1.6%
0.26 4
6.6%
0.27 2
3.3%
ValueCountFrequency (%)
0.66 1
 
1.6%
0.63 2
3.3%
0.6 2
3.3%
0.58 2
3.3%
0.53 2
3.3%
0.52 2
3.3%
0.5 1
 
1.6%
0.48 3
4.9%
0.47 1
 
1.6%
0.45 1
 
1.6%

Proanthocyanins
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5216393
Minimum0.41
Maximum3.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:42.541606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.64
Q11.31
median1.46
Q31.77
95-th percentile2.49
Maximum3.28
Range2.87
Interquartile range (IQR)0.46

Descriptive statistics

Standard deviation0.52188179
Coefficient of variation (CV)0.34297338
Kurtosis1.7285658
Mean1.5216393
Median Absolute Deviation (MAD)0.3
Skewness0.60580274
Sum92.82
Variance0.2723606
MonotonicityNot monotonic
2023-11-28T13:23:42.737538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1.35 4
 
6.6%
1.46 3
 
4.9%
1.77 3
 
4.9%
1.4 3
 
4.9%
1.63 3
 
4.9%
1.42 2
 
3.3%
1.56 2
 
3.3%
1.04 2
 
3.3%
2.08 2
 
3.3%
0.94 2
 
3.3%
Other values (34) 35
57.4%
ValueCountFrequency (%)
0.41 1
1.6%
0.42 1
1.6%
0.62 1
1.6%
0.64 1
1.6%
0.83 1
1.6%
0.94 2
3.3%
0.95 1
1.6%
1.03 1
1.6%
1.04 2
3.3%
1.05 1
1.6%
ValueCountFrequency (%)
3.28 1
1.6%
2.76 1
1.6%
2.5 1
1.6%
2.49 1
1.6%
2.35 1
1.6%
2.08 2
3.3%
2.01 1
1.6%
1.99 1
1.6%
1.95 1
1.6%
1.9 1
1.6%

Color_Intensity
Real number (ℝ)

Distinct46
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4140983
Minimum1.74
Maximum9.899999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:44.247537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.74
5-th percentile1.95
Q12.6
median3
Q33.8
95-th percentile6
Maximum9.899999
Range8.159999
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.4906456
Coefficient of variation (CV)0.43661473
Kurtosis5.9314613
Mean3.4140983
Median Absolute Deviation (MAD)0.55
Skewness2.165043
Sum208.26
Variance2.2220244
MonotonicityNot monotonic
2023-11-28T13:23:44.585038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
3.05 3
 
4.9%
2.5 2
 
3.3%
2.65 2
 
3.3%
2.9 2
 
3.3%
3.25 2
 
3.3%
2.7 2
 
3.3%
1.95 2
 
3.3%
2.8 2
 
3.3%
2.6 2
 
3.3%
2.45 2
 
3.3%
Other values (36) 40
65.6%
ValueCountFrequency (%)
1.74 1
1.6%
1.9 1
1.6%
1.95 2
3.3%
2 1
1.6%
2.06 2
3.3%
2.12 1
1.6%
2.15 1
1.6%
2.2 1
1.6%
2.4 1
1.6%
2.45 2
3.3%
ValueCountFrequency (%)
9.899999 1
1.6%
7.6 1
1.6%
7.1 1
1.6%
6 1
1.6%
5.75 1
1.6%
5.7 1
1.6%
5.45 1
1.6%
4.68 1
1.6%
4.6 1
1.6%
4.5 1
1.6%

Hue
Real number (ℝ)

Distinct44
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0312459
Minimum0.57
Maximum1.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:44.953913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.57
5-th percentile0.69
Q10.9
median1.04
Q31.16
95-th percentile1.36
Maximum1.71
Range1.14
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation0.21659799
Coefficient of variation (CV)0.21003525
Kurtosis0.65123663
Mean1.0312459
Median Absolute Deviation (MAD)0.14
Skewness0.31078128
Sum62.906
Variance0.046914689
MonotonicityNot monotonic
2023-11-28T13:23:45.227575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1.23 4
 
6.6%
1.04 3
 
4.9%
0.86 3
 
4.9%
1.16 3
 
4.9%
0.96 3
 
4.9%
0.95 2
 
3.3%
0.8 2
 
3.3%
1.05 2
 
3.3%
1 2
 
3.3%
0.93 2
 
3.3%
Other values (34) 35
57.4%
ValueCountFrequency (%)
0.57 1
 
1.6%
0.58 1
 
1.6%
0.66 1
 
1.6%
0.69 1
 
1.6%
0.73 1
 
1.6%
0.75 1
 
1.6%
0.76 1
 
1.6%
0.79 1
 
1.6%
0.8 2
3.3%
0.86 3
4.9%
ValueCountFrequency (%)
1.71 1
 
1.6%
1.45 1
 
1.6%
1.38 1
 
1.6%
1.36 1
 
1.6%
1.33 1
 
1.6%
1.31 1
 
1.6%
1.28 1
 
1.6%
1.27 1
 
1.6%
1.25 1
 
1.6%
1.23 4
6.6%

OD280
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6629508
Minimum1.29
Maximum3.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:45.527707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.29
5-th percentile1.55
Q12.27
median2.77
Q33.16
95-th percentile3.48
Maximum3.69
Range2.4
Interquartile range (IQR)0.89

Descriptive statistics

Standard deviation0.62326919
Coefficient of variation (CV)0.23405208
Kurtosis-0.59069554
Mean2.6629508
Median Absolute Deviation (MAD)0.44
Skewness-0.54720123
Sum162.44
Variance0.38846448
MonotonicityNot monotonic
2023-11-28T13:23:45.953470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.96 3
 
4.9%
2.78 2
 
3.3%
2.44 2
 
3.3%
1.82 2
 
3.3%
2.87 2
 
3.3%
3.3 2
 
3.3%
3.21 2
 
3.3%
2.26 2
 
3.3%
2.31 2
 
3.3%
2.77 2
 
3.3%
Other values (40) 40
65.6%
ValueCountFrequency (%)
1.29 1
1.6%
1.36 1
1.6%
1.51 1
1.6%
1.55 1
1.6%
1.58 1
1.6%
1.59 1
1.6%
1.63 1
1.6%
1.67 1
1.6%
1.82 2
3.3%
2.06 1
1.6%
ValueCountFrequency (%)
3.69 1
1.6%
3.63 1
1.6%
3.57 1
1.6%
3.48 1
1.6%
3.39 1
1.6%
3.38 1
1.6%
3.33 1
1.6%
3.3 2
3.3%
3.28 1
1.6%
3.26 1
1.6%

Proline
Real number (ℝ)

Distinct47
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean537.98361
Minimum278
Maximum1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size976.0 B
2023-11-28T13:23:46.188814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile315
Q1428
median500
Q3650
95-th percentile870
Maximum1015
Range737
Interquartile range (IQR)222

Descriptive statistics

Standard deviation163.93489
Coefficient of variation (CV)0.30472099
Kurtosis0.33761347
Mean537.98361
Median Absolute Deviation (MAD)125
Skewness0.70777196
Sum32817
Variance26874.65
MonotonicityNot monotonic
2023-11-28T13:23:46.384181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
680 3
 
4.9%
450 3
 
4.9%
625 3
 
4.9%
495 3
 
4.9%
630 2
 
3.3%
510 2
 
3.3%
520 2
 
3.3%
345 2
 
3.3%
428 2
 
3.3%
562 2
 
3.3%
Other values (37) 37
60.7%
ValueCountFrequency (%)
278 1
1.6%
290 1
1.6%
312 1
1.6%
315 1
1.6%
325 1
1.6%
342 1
1.6%
345 2
3.3%
352 1
1.6%
355 1
1.6%
378 1
1.6%
ValueCountFrequency (%)
1015 1
 
1.6%
937 1
 
1.6%
886 1
 
1.6%
870 1
 
1.6%
750 1
 
1.6%
718 1
 
1.6%
714 1
 
1.6%
710 1
 
1.6%
695 1
 
1.6%
680 3
4.9%

kmeans_labels
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size976.0 B
2
61 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters61
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 61
100.0%

Length

2023-11-28T13:23:46.582232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T13:23:46.757421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 61
100.0%

Most occurring characters

ValueCountFrequency (%)
2 61
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 61
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 61
100.0%

Interactions

2023-11-28T13:23:36.686872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:09.737675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:11.808003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:13.673592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:15.796175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:18.990853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:21.538399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:23.556463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:25.447756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:27.361089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:29.375544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:32.135842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:34.694853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:36.855299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:09.900178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:11.955316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:13.856190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:15.973956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:19.201390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:21.705086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:23.712691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:25.598563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:27.522591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:29.551425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:32.399594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:34.865378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:37.023591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:10.046295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:12.082330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:13.997977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:16.129372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:19.483600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:21.838009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:23.837830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:25.732731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:27.675585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:29.701496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:32.678319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:34.992270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:37.183975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:10.223284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:12.224893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:14.159438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:16.288109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:19.809448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:22.007441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:23.993914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:25.877253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:27.828160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:29.873069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:32.911056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:35.150913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:37.351305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:10.397067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:12.368119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:14.362090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:16.449306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:20.096089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:22.169014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:24.163889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:26.034669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:27.983756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:30.039569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:33.193453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:35.317922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:37.484323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:10.548767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:12.496038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:14.503899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:16.591512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:20.258266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:22.310506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:24.309061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:26.186909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:28.127357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:30.217310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:33.456489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:35.442464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:37.635119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:10.706864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:12.636096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:14.660012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:16.744759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:20.445390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:22.451483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:24.445271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:26.336303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:28.286487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:30.376981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:33.636593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:35.597614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:37.777688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:10.857399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:12.769964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:14.822981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:16.906103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:20.614489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:22.589221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:24.573766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:26.466547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:28.423761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:30.522531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:33.785852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:35.749446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:37.924106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:11.003884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:12.909995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:14.971340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:17.069817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:20.759024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:22.735195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:24.714768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:26.603981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:28.575855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:30.751322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:33.931691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:35.896722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:38.073454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:11.168493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:13.062325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:15.137915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:17.229917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:20.912935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:22.893676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:24.860451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:26.751968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:28.736066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:31.053586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:34.087567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:36.036174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:38.241280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:11.337287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:13.237448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:15.315174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:17.412140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:21.095497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:23.079554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:25.020120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:26.912336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:28.894262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:31.321089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:34.259265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:36.216766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:38.407715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:11.483795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:13.380100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:15.461484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:17.565080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:21.240180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:23.243931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:25.170440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:27.057914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:29.048094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:31.595763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:34.402158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:36.380796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:38.541721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:11.633487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:13.512630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:15.623697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:17.719722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:21.386412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:23.388398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:25.302730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:27.206062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:29.223963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:31.822135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:34.534917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:23:36.526721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-28T13:23:46.881118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AlcoholAshAsh_AlcanityColor_IntensityFlavanoidsHueMagnesiumMalic_AcidNonflavanoid_PhenolsOD280ProanthocyaninsProlineTotal_Phenols
Alcohol1.000-0.283-0.2240.319-0.205-0.280-0.000-0.066-0.043-0.268-0.1820.138-0.284
Ash-0.2831.0000.4830.0860.140-0.0940.1170.1850.2430.0180.0250.0630.033
Ash_Alcanity-0.2240.4831.000-0.2340.126-0.218-0.2600.2770.2930.2180.065-0.233-0.002
Color_Intensity0.3190.086-0.2341.000-0.018-0.2650.188-0.0080.100-0.270-0.1370.300-0.092
Flavanoids-0.2050.1400.126-0.0181.0000.139-0.0760.013-0.3160.6190.552-0.2360.821
Hue-0.280-0.094-0.218-0.2650.1391.0000.001-0.282-0.1360.1170.1160.0440.117
Magnesium-0.0000.117-0.2600.188-0.0760.0011.0000.041-0.195-0.2080.0270.249-0.049
Malic_Acid-0.0660.1850.277-0.0080.013-0.2820.0411.0000.1970.080-0.194-0.170-0.162
Nonflavanoid_Phenols-0.0430.2430.2930.100-0.316-0.136-0.1950.1971.000-0.340-0.249-0.139-0.355
OD280-0.2680.0180.218-0.2700.6190.117-0.2080.080-0.3401.0000.342-0.1490.561
Proanthocyanins-0.1820.0250.065-0.1370.5520.1160.027-0.194-0.2490.3421.0000.0570.349
Proline0.1380.063-0.2330.300-0.2360.0440.249-0.170-0.139-0.1490.0571.000-0.191
Total_Phenols-0.2840.033-0.002-0.0920.8210.117-0.049-0.162-0.3550.5610.349-0.1911.000

Missing values

2023-11-28T13:23:38.798680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-28T13:23:39.135124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Prolinekmeans_labels
2312.851.602.5217.8952.482.370.261.463.931.0903.6310152
5912.370.941.3610.6881.980.570.280.421.951.0501.825202
6012.331.102.2816.01012.051.090.630.413.271.2501.676802
6112.641.362.0216.81002.021.410.530.625.750.9801.594502
6312.371.132.1619.0873.503.100.191.874.451.2202.874202
6412.171.452.5319.01041.891.750.451.032.951.4502.233552
6512.371.212.5618.1982.422.650.372.084.601.1902.306782
6712.371.171.9219.6782.112.000.271.044.681.1203.485102
6912.211.191.7516.81511.851.280.142.502.851.2803.077182
7012.291.612.2120.41031.101.020.371.463.050.9061.828702
AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Prolinekmeans_labels
12512.072.162.1721.0852.602.650.371.352.7600000.863.283782
12612.431.532.2921.5862.743.150.391.773.9400000.692.843522
12711.792.132.7828.5922.132.240.581.763.0000000.972.444662
12812.371.632.3024.5882.222.450.401.902.1200000.892.783422
13012.861.352.3218.01221.511.250.210.944.1000000.761.296302
13212.812.312.4024.0981.151.090.270.835.7000000.661.365602
13412.511.242.2517.5852.000.580.601.255.4500000.751.516502
13512.602.462.2018.5941.620.660.630.947.1000000.731.586952
15412.581.292.1020.01031.480.580.531.407.6000000.581.556402
17112.772.392.2819.5861.390.510.480.649.8999990.571.634702